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Anderson DM, Cronk R, Best L, Radin M, Schram H, Tracy JW, Bartram J. Budgeting for Environmental Health Services in Healthcare Facilities: A Ten-Step Model for Planning and Costing. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E2075. [PMID: 32245057 PMCID: PMC7143484 DOI: 10.3390/ijerph17062075] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 03/13/2020] [Accepted: 03/17/2020] [Indexed: 11/24/2022]
Abstract
Environmental health services (EHS) in healthcare facilities (HCFs) are critical for safe care provision, yet their availability in low- and middle-income countries is low. A poor understanding of costs hinders progress towards adequate provision. Methods are inconsistent and poorly documented in costing literature, suggesting opportunities to improve evidence. The goal of this research was to develop a model to guide budgeting for EHS in HCFs. Based on 47 studies selected through a systematic review, we identified discrete budgeting steps, developed codes to define each step, and ordered steps into a model. We identified good practices based on a review of additional selected guidelines for costing EHS and HCFs. Our model comprises ten steps in three phases: planning, data collection, and synthesis. Costing-stakeholders define the costing purpose, relevant EHS, and cost scope; assess the EHS delivery context; develop a costing plan; and identify data sources (planning). Stakeholders then execute their costing plan and evaluate the data quality (data collection). Finally, stakeholders calculate costs and disseminate findings (synthesis). We present three hypothetical costing examples and discuss good practices, including using costing frameworks, selecting appropriate indicators to measure the quantity and quality of EHS, and iterating planning and data collection to select appropriate costing approaches and identify data gaps.
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Affiliation(s)
- Darcy M. Anderson
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - Ryan Cronk
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - Lucy Best
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - Mark Radin
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - Hayley Schram
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - J. Wren Tracy
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
| | - Jamie Bartram
- The Water Institute, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; (R.C.); (L.B.); (M.R.); (H.S.); (J.W.T.); (J.B.)
- School of Civil Engineering, University of Leeds, Leeds LS2 9JT, UK
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